Since the pioneering work of Oren et al. 1998several attempts have been made to predict relative permeability curves with Digital Rock Physics (DRP) technique. However, the problem has proved more complex than what researchers have expected, and these attempts failed. One of the main issues was the high number of uncertain parameters especially for the wettability input and this gets worst in mixed-wet scenario as the number of parameters is higher than in water-wet and oil-wet cases. In fact, Sorbie and Skauge 2012 stated that wettability assignment is the most complex and least validated stage in DRP simulation work ow. Similarly, Bondino et al. 2013concluded that "genuine prediction" of multi-phase ow properties will remain not credible until important progress is achieved in the area of wettability characterization at the pore scale.In this work, we propose a pragmatic approach to tackle these problems. First, we parallelize our pore network simulator in order to achieve large scale PNM simulations. Then, we develop an innovative and fast anchoring experiment imaged by micro-CT scanner, that helps to determine several wettability parameters needed for the DRP simulation (including the fraction of oil-wet/water-wet pores, any spatial or radius correlation of oil wet pores…). This experiment also provides an estimation of macroscopic parameters that help to anchor our pore scale simulations and further reduce the uncertainty. In addition to help reducing the uncertainty of the simulation, this experiment provides a fast estimation of the wettability of the system. Images representing large volumes with low resolution are, rst, improved with Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) to obtain a large image with high resolution. Then, a pore network is extracted, and TotalEnergies parallel pore network simulator is used for multiphase ow simulations considering the constraints from the anchoring experiment to reduce the uncertainty. Finally, we compare our simulations against high quality SCAL experiment performed inhouse and we assess the predictive power of our DRP work ow.
Article HighlightsIn this paper, a new methodology to determine wettability input for pore scale simulation is developed.Wettability was characterized and found correlated to the radii of the pores and spatial correlation of wettability was observed. The pore scale simulation coupled with the wettability anchoring experiment was found to be able to predict the results of SCAL experiment performed on the same rock with the same uids.characterization Skauge 2012, Bondino et al. 2013) . DRP could also be criticized for computing properties on usually small rock volumes without proving that the Representative Elementary Volume (REV) for single phase and two-phase ow is reached or that the simulations are not dominated by nite size and boundary effects. In a previous work (Regaieg et al. 2022), we have used ESRGAN method in order to increase the resolution of our images and have large images with good resolution. We ha...
Digital Rock Physics (DRP) provides a fast way to compute rock properties and carry-out related sensitivity analysis to complement laboratory measurements. In DRP, the first step is to obtain micro-CT images of a rock, this is then followed by segmenting the images to distinguish the rock from the pore space, and finally flow simulations are performed to compute advanced rock properties such as relative permeability and capillary pressure.
During image acquisition, a compromise is often made between the speed of the image acquisition, the size of the scanned volume and the resolution obtained: increasing the resolution decreases the field of view, in turn limiting the quantity of information obtained from the image and thus making DRP simulations less representative. Furthermore, the geometry of a real rock is not always well characterized, notably due to the lack of image resolution which in turn introduces uncertainty in the pore/throat geometry and consequently introduces errors in rock property computations
Recent advances in deep learning methods have led to major advances in computer vision techniques, and notably in the field of super-resolution imaging. In this work, we present such a strategy to digitally increase the resolution of 3D micro-CT using a deep learning approach called Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). This allows us to have well resolved images with large field of view. Large super-resolved images were produced for resolution improvement factors of x4 and x8 in each direction. The super-resolved images were more realistic visually and produced better single and multiphase flow simulations results.
In order to enable the simulations of very large images generated by ESRGAN we describe a stitching strategy that we have developed in order to enable the extraction of pore networks on such large images and present several validation cases of this method. This approach enables the extraction of pore networks from large images (3184*3280*12928 voxels image) that are needed to achieve large scale DRP simulations.
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